scholarly journals Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
S. Puga-Guzmán ◽  
J. Moreno-Valenzuela ◽  
V. Santibáñez
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Gravitational Force ◽  
Tracking Accuracy ◽  
Adaptive Neural Network ◽  
Tracking Errors ◽  
The Neural Network ◽  
Proportional Derivative Controller ◽  
Velocity Tracking ◽  
Uniformly Bounded

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

scholarly journals Distributed Adaptive Coordinated Control of Multiple Euler–Lagrange Systems considering Output Constraints and Time Delays

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Hongde Qin ◽  
Xiaojia Li ◽  
Yanchao Sun
Keyword(s):  
Coordinated Control ◽  
Tracking Accuracy ◽  
Tracking Errors ◽  
Simulation Experiments ◽  
Control Laws ◽  
The Neural Network ◽  
Output Constraints ◽  
Control Schemes ◽  
Virtual Leader ◽  
Coordinated Tracking

In this paper, we mainly investigate the coordinated tracking control issues of multiple Euler–Lagrange systems considering constant communication delays and output constraints. Firstly, we devise a distributed observer to ensure that every agent can get the information of the virtual leader. In order to handle uncertain problems, the neural network technique is adopted to estimate the unknown dynamics. Then, we utilize an asymmetric barrier Lyapunov function in the control design to guarantee the output errors satisfy the time-varying output constraints. Two distributed adaptive coordinated control schemes are proposed to guarantee that the followers can track the leader accurately. The first scheme makes the tracking errors between followers and leader be uniformly ultimately bounded, and the second scheme further improves the tracking accuracy. Finally, we utilize a group of manipulator networks simulation experiments to verify the validity of the proposed distributed control laws.

Solution to the Forward and Inverse Kinematic Equations Using Multilayer Back-Propagation Neural Network

1994 ◽  
Author(s):  
Phani K. Nagarjuna ◽  
Athamaram H. Soni
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Robot Manipulator ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Inverse Kinematic ◽  
Robot Arm ◽  
Kinematic Equation ◽  
Neural Network Approach ◽  
The Neural Network

Abstract The problem of inverse kinematics in Robotics, is a nonlinear mapping from a given cartesian coordinates to the desirable joint coordinates of the robot arm. It is found that an appropriately designed neural network can be trained to learn the non-linearity of the Inverse Kinematic Equation (IKE). We present an approach for solving the Forward Kinematic Equation (FKE) and the IKE by means of a Multi Layer Back-Propagation Neural Network (Rumelhart et al., 1986). The neural network approach is applied to a Two Degrees-of-Freedom (DOF) robot manipulator and the results are compared with those obtained using the analytical solution. The results obtained from the simulation of the neural network indicate a fairly accurate learning of the FKE and IKE by the Multi Layer Back-Propagation Neural Network.

scholarly journals Robust composite neural dynamic surface control for the path following of unmanned marine surface vessels with unknown disturbances

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878664 ◽  
Author(s):  
Jiangfeng Zeng ◽  
Lei Wan ◽  
Yueming Li ◽  
Ziyang Zhang ◽  
Yufei Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Path Following ◽  
Dynamic Surface Control ◽  
Prediction Errors ◽  
Tracking Accuracy ◽  
Tracking Errors ◽  
Dynamic Surface ◽  
Surface Control ◽  
Surface Vessels ◽  
Marine Surface

This article presents a robust composite neural-based dynamic surface control design for the path following of unmanned marine surface vessels in the presence of nonlinearly parameterized uncertainties and unknown time-varying disturbances. Compared with the existing neural network-based dynamic surface control methods where only the tracking errors are commonly used for the neural network weight updating, the proposed scheme employs both the tracking errors and the prediction errors to construct the adaption law. Therefore, faster identification of the system dynamics and improved tracking accuracy are achieved. In particular, an outstanding advantage of the proposed neural network structure is simplicity. No matter how many neural network nodes are utilized, only one adaptive parameter that needs to be tuned online, which effectively reduces the computational burden and facilitates to implement the proposed controller in practice. The uniformly ultimate boundedness stability of the closed-loop system is established via Lyapunov analysis. Comparison studies are presented to demonstrate the effectiveness of the proposed composite neural-based dynamic surface control architecture.

Adaptive Neural Network Compensating Control for Coordinated Motion of Space-Based Robot System With Uncertain Parameters and External Disturbances

2009 ◽  
Author(s):  
Chen Zhiyong ◽  
Chen Li
Keyword(s):  
Neural Network ◽  
System Stability ◽  
Uncertain Parameters ◽  
Coordinated Motion ◽  
Adaptive Neural Network ◽  
Robot System ◽  
External Disturbances ◽  
Neural Network Controller ◽  
The Neural Network ◽  
On Line

The control problem of space-based robot system with uncertain parameters and external disturbances is considered. With Lagrangian formulation and augmentation approach, the dynamic equations of space-based robot system in workspace are derived. Based on the results, an adaptive neural network compensating control scheme for coordinated motion between the base’s attitude and end-effector of space-based robot system is developed. It is based on the inertia-related method, and incorporates a neural network controller to compensate the uncertainties. The closed-loop system stability with the neural network adapted on-line is discussed in detail through the Lyapunov stability approach. Comparing with many adaptive and robust control schemes, the controller proposed does not require one to determine the regression matrix for space robot system and then avoids tedious computations. Numerical simulations are provided to show the effectiveness of the approach.

scholarly journals Assignment of Focus Position with Convolutional Neural Networks in Adaptive Lens Based Axial Scanning for Confocal Microscopy

2022 ◽  
Vol 12 (2) ◽  
pp. 661
Author(s):  
Katharina Schmidt ◽  
Nektarios Koukourakis ◽  
Jürgen W. Czarske
Keyword(s):  
Neural Network ◽  
Applied Voltage ◽  
Laser Scanning ◽  
Degrees Of Freedom ◽  
Single Shot ◽  
Training Procedure ◽  
Focus Position ◽  
The Neural Network ◽  
Complex Adaptive ◽  
Axial Scanning

Adaptive lenses offer axial scanning without mechanical translation and thus are promising to replace mechanical-movement-based axial scanning in microscopy. The scan is accomplished by sweeping the applied voltage. However, the relation between the applied voltage and the resulting axial focus position is not unambiguous. Adaptive lenses suffer from hysteresis effects, and their behaviour depends on environmental conditions. This is especially a hurdle when complex adaptive lenses are used that offer additional functionalities and are controlled with more degrees of freedom. In such case, a common approach is to iterate the voltage and monitor the adaptive lens. Here, we introduce an alternative approach which provides a single shot estimation of the current axial focus position by a convolutional neural network. We use the experimental data of our custom confocal microscope for training and validation. This leads to fast scanning without photo bleaching of the sample and opens the door to automatized and aberration-free smart microscopy. Applications in different types of laser-scanning microscopes are possible. However, maybe the training procedure of the neural network must be adapted for some use cases.

scholarly journals Decoupling Control for Three Degrees of Freedom Servo System Based on Neural Network

2013 ◽  
Vol 765-767 ◽  
pp. 1840-1843
Author(s):  
Zhen Bi Li ◽  
Bai Ting Zhao ◽  
Yuan Yuan Jiang
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Inertial Navigation System ◽  
Servo System ◽  
Good Control ◽  
Decoupling Control ◽  
Simulating Experiment ◽  
Control Precision ◽  
The Neural Network ◽  
Three Degrees Of Freedom

Three degrees of freedom servo system (TDFSS) is one of the key equipments of inertial testing, such as evaluation of inertial navigation system and test of inertial components. It is a kind of servo system with some non-linearity and uncertainty. This thesis takes advantage of the characteristic of Neural network in approaching non-linear function, applies the Neural network on the three-axis simulator, provides a method for the TDFSS. Simulating experiment has been used to verify the advantage of the scheme and achieved completely decoupling control. The scheme gives good control precision, and it is simply structured and easily implemented.Introduction

scholarly journals MODELING QUANTUM MECHANICAL OBSERVERS VIA NEURAL-GLIAL NETWORKS

2012 ◽  
Vol 26 (09) ◽  
pp. 1250060 ◽  
Author(s):  
EIJI KONISHI
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Quantum Mechanical ◽  
Classical Information ◽  
The Neural Network ◽  
Large N ◽  
Quantum Hamiltonian ◽  
Synaptic Junctions ◽  
The Brain ◽  
Novel Model

We investigate the theory of observers in the quantum mechanical world by using a novel model of the human brain which incorporates the glial network into the Hopfield model of the neural network. Our model is based on a microscopic construction of a quantum Hamiltonian of the synaptic junctions. Using the Eguchi–Kawai large N reduction, we show that, when the number of neurons and astrocytes is exponentially large, the degrees of freedom (d.o.f) of the dynamics of the neural and glial networks can be completely removed and, consequently, that the retention time of the superposition of the wavefunctions in the brain is as long as that of the microscopic quantum system of pre-synaptics sites. Based on this model, the classical information entropy of the neural-glial network is introduced. Using this quantity, we propose a criterion for the brain to be a quantum mechanical observer.

Robust Neural-Network Hybrid Tracking Control of Underwater Vehicles

2008 ◽  
Author(s):  
Wei-Lin Luo ◽  
Zao-Jian Zou ◽  
Lan-Ping Huang
Keyword(s):  
Neural Network ◽  
Autonomous Underwater Vehicle ◽  
Underactuated System ◽  
Tracking Errors ◽  
Actuator Dynamics ◽  
The Neural Network ◽  
Modeling Errors ◽  
Random Disturbances ◽  
On Line ◽  
Bounded Disturbances

A cascade system of an autonomous underwater vehicle is considered. It consists of the nonlinear equation of motion and the equations of actuator dynamics. In the motion equation, unmatched uncertainties are taken into account, including the modeling errors and the bounded disturbances. The modeling errors result from the parameter errors, the ignored high-order modes and unmodelled dynamics. The bounded disturbances refer to environment forces or unknown random disturbances. To obtain accurate manoeuvring of the underactuated system, a hybrid robust controller is proposed by using backstepping Lyapunov functions. A two-layer feedforward neural-network is applied to compensate the modeling errors and the derivatives of desired control inputs, while the H∞ control strategy is used to achieve the L2-gain performance with respect to the bounded disturbances. The on-line tuning algorithms of the neural-network weights are given. The uniformly ultimately bounded stabilities of the tracking errors and the neural-network weights errors are analyzed. Moreover, selection of the gains in controller is recommended by analysis of the upper boundedness of errors. Simulation results have demonstrated the validity of the controller proposed.

Neuroadaptive control of elastic-joint robots using robust performance enhancement

Robotica ◽  
2001 ◽  
Vol 19 (6) ◽  
pp. 619-629 ◽  
Author(s):  
C.J.B. Macnab ◽  
G.M.T. D'Eleuterio
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Performance Enhancement ◽  
Control Law ◽  
Network Stability ◽  
Elastic Joint ◽  
Lyapunov Techniques ◽  
The Neural Network ◽  
Control Scheme ◽  
Neuroadaptive Control

A neuroadaptive control scheme for elastic-joint robots is proposed that uses a relatively small neural network. Stability is achieved through standard Lyapunov techniques. For added performance, robust modifications are made to both the control law and the weight update law to compensate for only approximate learning of the dynamics. The estimate of the modeling error used in the robust terms is taken directly from the error of the network in modeling the dynamics at the currant state. The neural network used is the CMAC-RBF Associative Memory (CRAM), which is a modification of Albus's CMAC network and can be used for robots with elastic degrees of freedom. This results in a scheme that is computationally practical and results in good performance.

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